Three types of data models and various data modeling techniques are available to data management teams to help convert data into valuable business information.
A well-planned data model is the foundation of strong, reliable systems that help businesses run smoothly and make smart decisions. Whether it’s for day-to-day operations or for analyzing business performance, a good data model turns raw data into meaningful information that brings real value.
Let’s take a closer look at what data models are, why they matter, and how they are used across different areas of a business.
What Is a Data Model?
A data model is a clear plan that explains how data is organized, stored, and connected. It helps show how pieces of data relate to each other and gives meaning to the data collected. In simple terms, a data model tells you who, what, where, when, and why — all in one structure.
For example, in a retail store, a data model might show:
- Who bought a product
- What they bought
- When the purchase happened
- Where it took place
- Why it matters (like linking it to a promotion or season)
This information can help a business improve customer service, stock better products, and run more efficient operations.
Types of Data Models
To build a useful data model, it’s best to follow a top-down approach, moving from big-picture planning to detailed design. There are three main types of data models used in this process:
1. Conceptual Data Model
- Focuses on what data is needed for business or reporting.
- It includes general concepts like customers, products, or transactions.
- It doesn’t show how or where the data is stored.
2. Logical Data Model
- Adds more detail to the conceptual model.
- Shows data structures, like tables, columns, and relationships.
- Independent of any specific software or database system.
3. Physical Data Model
- Shows exactly how data will be stored in a database or file system.
- Includes tables, indexes, data types, and keys.
- Built for a specific tool or system.
Popular Data Modeling Techniques
Over time, data modeling has grown and changed. Let’s go over the main types of data models that have been used—from the old methods to the ones we mostly use today.
1. Hierarchical Data Model
- Data is arranged like a tree, with a parent and its child records.
- A child can have only one parent.
- Was mostly used in early mainframe systems.
2. Network Data Model
- Like the hierarchical model, but a child can have many parents.
- Data is connected more like a web than a tree.
- Was widely used before relational databases became common.
3. Relational Data Model
- The most common model today.
- Data is stored in tables with rows and columns.
- Relationships between data are defined by keys and rules.
4. Object-Oriented Data Model
- Combines ideas from object-oriented programming and data modeling.
- Data and actions related to the data are stored together as objects.
- Includes concepts like classes and inheritance.
5. Entity-Relationship (ER) Model
- Focuses on entities (like a customer or product), their attributes, and relationships.
- Widely used in transactional systems.
- Helps reduce duplicate data and keeps things organized.
6. Dimensional Data Model
- Used mainly in business intelligence (BI) and data analysis.
- Divides data into facts (numbers or totals) and dimensions (descriptive details).
- Often used in star schemas for easier reporting.
7. Graph Data Model
- Used to model complex connections in graph databases.
- Uses nodes (data points) and edges (relationships).
- Great for showing social networks, web links, or supply chains.
Best Practices for Building Data Models
To get the most value from your data, here are some proven tips for creating data models:
1. Use Data Models as Blueprints
- Treat data models like building plans.
- Use them to guide software teams, analysts, and data managers.
2. Collect Business and Data Requirements Early
- Talk to business leaders and data experts to understand what the business needs.
- Use their input to shape your models properly.
3. Build in Small Steps
- Don’t try to model everything at once.
- Divide the project into smaller areas and work on them one by one.
4. Use the Right Tools
- Data modeling tools make it easier to design, update, and document models.
- Many tools also help you connect your models with other systems like BI tools or data catalogs.
5. Keep Data Detailed
- Store data at the lowest level possible (called granularity).
- Summarize only when needed but keep the original detail.
6. Limit Redundant Data
- Avoid too much denormalization (repeating data to speed up queries).
- Redundant data can cause problems in reporting and updates.
7. Share Models with Business Users
- Don’t hide your models from the business team.
- Use visual models to explain data and get their feedback.
8. Treat Models Like Software Code
- Manage and protect your models the same way you do your applications.
- This helps avoid messy systems and supports long-term success.
Final Thoughts
Data modeling is not just for technical teams. It’s a powerful tool that helps businesses make sense of their data and turn it into useful information. By following clear steps and using the right approach, companies can build strong data models that support decision-making, improve operations, and prepare for future growth.
As technology evolves, so will data modeling. But the core idea remains the same: give meaning to data, and it becomes a valuable asset.